Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors

By Stephen Grossberg. 1976

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Deep learning in neural networks: An overview (2014, neutral): “…1992; Baldi & Hornik, 1989; Barlow, Kaushal, & Mitchison, 1989; Barrow, 1987; Deco & Parra, 1997; Field, 1987; Földiák, 1990; Földiák & Young, 1995; Grossberg, 1976a, 1976b; Hebb, 1949; Kohonen, 1972, 1982, 1988; Kosko, 1990; Martinetz, Ritter, & Schulten, 1990; Miller, 1994; Mozer, 1991; Oja,…”
From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation (2012, background): “, 1997), corroborating detailed rate-based (Grossberg, 1976; Rauschecker and Singer, 1981; Oja, 1982; Bienenstock et al., 1982; Gerstner and Kistler, 2002a) and spiking neural models (van Rossum et al.”
Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation (2014, methodology): “The model uses the adaptive resonance theory (ART) mechanism [69] for extracting informative intermediate level visual features.”
Tracelink: A model of consolidation and amnesia (2005, unknown): “The stability-plasticity dilemma has been addressed by relatively few researchers (Carpenter & Grossberg, 1988; Grossberg, 1976, 1987; Hasselmo, 1994; Hasselmo, Bodelón, & Wyble, 2002; Meeter, Talamini, & Murre, in press; Murre, 1992).”
Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS (2012, methodology): “…maps (SOM), associative memories, learning vector quantization networks (LVQ, Kohonen 1988), and diﬀerent types of adaptive resonance theory networks (ART, Grossberg 1988), namely ART1 (Carpenter and Grossberg 1987b), ART2 (Carpenter and Grossberg 1987a), and ARTMAP (Carpenter et al. 1991) nets.”
A Theory of Visual Coding : Memory , and Development (2010, unknown): “This paper, along with [6], indicates that a purely chemically defined critical period for developmental tuning can have unpleasant coding properties.”
A revised model of short-term memory and long-term learning of verbal sequences (2006, methodology): “In Hebb’s (1961) procedure, random sequences of digits were presented for ISR, but unannounced to participants, the same list was presented every third trial.”
Adaptive pattern classification and universal recoding: II. Feedback, expectation, olfaction, illusions (1976, methodology): “The mechanism that does this therefore nonspecifically influences all the items coded in the neural field; cf., Grossberg (1976b).”
2005 Special Issue (2005, unknown): “The successful learning rule for spiking neurons can sample some function q of this product (Grossberg, 1974; 1976a,b) or postsyn-aptically gated decay Grossberg instar (Grossberg, 1974; 1976a,b) in order to encode both spatial and temporal components of the pattern.”
Forming sparse representations by local anti-Hebbian learning (1990, background): “Competitive learning (Malsburg 1973; Grossberg 1976) in its simplest version (Rumelhart and Zipser 1985) activates only the unit that fits the input pattern best by selecting the one with the largest weighted sum and suppressing the output of all other units.”